{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/33118"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/33118","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Adaptive Beamforming using ICA for Target Identification in Noisy Environments","abstract":"The blind source separation problem has received a great deal of attention in previous years. The aim of this problem is to estimate a set of original source signals from a set of linearly mixed signals through any number of signal processing techniques. While many methods exist that attempt to solve the blind source separation problem, a new technique is being used that uniquely separates audio sources as they are received from a microphone array. In this thesis a new algorithm is proposed that that utilizes the ICA algorithm in conjunction with a filtering technique that separates source signals and then removes sources of interference so that a signal of interest can be accurately tracked. Experimental results will compare a common blind source separation technique to the new algorithm and show that the new algorithm can detect a signal of interest and accurately track it as it moves through an anechoic environment.","abstract_html":"The blind source separation problem has received a great deal of attention in previous years. The aim of this problem is to estimate a set of original source signals from a set of linearly mixed signals through any number of signal processing techniques. While many methods exist that attempt to solve the blind source separation problem, a new technique is being used that uniquely separates audio sources as they are received from a microphone array. In this thesis a new algorithm is proposed that that utilizes the ICA algorithm in conjunction with a filtering technique that separates source signals and then removes sources of interference so that a signal of interest can be accurately tracked. Experimental results will compare a common blind source separation technique to the new algorithm and show that the new algorithm can detect a signal of interest and accurately track it as it moves through an anechoic environment.","abstract_has_math":false,"creators":["Wiltgen, Timothy Edward"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Mechanical Engineering","degree_department":"Mechanical Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Roan, Michael J."],"committee_members":["Carneal, James P.","Fuller, Christopher R."],"year":2007,"date_issued":"2007-05-09","date_published":"2007-05-09","updated_at":"2026-07-22T22:18:44Z","subjects":["Wiener Filter","ICA","MVDR","Microphone Array","Adaptive Beamforming"],"languages":[],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05222007-102626"],"render_values":[{"text":"etd-05222007-102626","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10919/33118","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Roan, Michael J."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Carneal, James P.","Fuller, Christopher R."]},{"key":"dc:contributor.department","label":"Department","values":["Mechanical Engineering"]},{"key":"dc:creator","label":"Author","values":["Wiltgen, Timothy Edward"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2014-03-14T20:38:17Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2014-03-14T20:38:17Z","2007-05-23"]},{"key":"dc:date.issued","label":"Date","values":["2007-05-09"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Wiener Filter","ICA","MVDR","Microphone Array","Adaptive Beamforming"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["etd-05222007-102626"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/10919/33118"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The blind source separation problem has received a great deal of attention in previous years. The aim of this problem is to estimate a set of original source signals from a set of linearly mixed signals through any number of signal processing techniques. While many methods exist that attempt to solve the blind source separation problem, a new technique is being used that uniquely separates audio sources as they are received from a microphone array. In this thesis a new algorithm is proposed that that utilizes the ICA algorithm in conjunction with a filtering technique that separates source signals and then removes sources of interference so that a signal of interest can be accurately tracked. 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